Special Issue "Satellite Remote Sensing Phenological Libraries"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Nikos Koutsias
E-Mail Website
Guest Editor
Department of Environmental Engineering, University of Patras, Seferi 2, 30100 Agrinio, Greece
Interests: geoinformatics on wildland fires; satellite remote sensing phenology, natural disasters and landscape ecology; applied multivariate methods, time-series data analysis, geostatistics and point pattern analysis with special emphasis on spatio-temporal analysis of wildland fire ignition points; positional uncertainty and data modeling
Dr. Alexandra Gemitzi
E-Mail Website
Guest Editor
Department of Environmental Engineering, Faculty of Engineering, Democritus University of Thrace, Xanthi, Greece
Interests: environmental modeling; remote sensing; groundwater–surface water interactions; GIS; climate change
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Dr. Sofia Bajocco
E-Mail Website
Guest Editor
Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing (CREA-IT), 00186 Rome, Italy
Interests: vegetation phenology dynamics; landscape disturbance; fire spatio-temporal behavior; land cover change processes; remotely sensed data analysis; geoprocessing techniques; multivariate statistical methods
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite remote sensing can provide the necessary data to estimate phenology, an important element of landscape that can be useful, especially for climate and land use change assessments, at the global, continental, regional or even local scales. Phenology data can be used for the assessment of vegetation types distribution, carbon budget quantification, evaluation of year-to-year spatial and temporal variations of vegetation seasonality, and the dependence of these variations on environmental factors. Phenology data sets are also important for estimation of primary productivity, ecosystem healthiness and they also serve as input to land surface models. Given the plethora of free satellite missions and the available products, either those coming from medium-to-high spatial resolution sensors, e.g. Landsat and Sentinel, or from moderate resolution sensors, e.g. MODIS, time series methods are becoming very popular approaches. Moreover, the advent of hyperspectral missions, like PRISMA and Venµs, is opening new possibilities to estimate phenological parameters, allowing more precise spectral diagnostic and quantitative monitoring of vegetation phenology status over larger areas.

Remote sensing phenology captures broad scale phenological patterns with high degree of homogeneity and standardization offered by the nature of remote sensing data. Remotely sensed phenological data can be useful for numerous applications covering fields like forestry, agriculture, climate, hazards, oceanography and inland waters, drought severity, and wildfire risk. Under this perspective, in this special issue we expect and welcome high quality manuscripts on the assessment and use of satellite remote sensing time series data and satellite remote sensing phenological libraries that can be used in any scientific domain and field.

Assoc. Prof. Dr. Nikos Koutsias
Assoc. Prof. Dr. Alexandra Gemitzi
Dr. Sofia Bajocco
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Phenology
  • Remote Sensing
  • Seasonality
  • Forestry
  • Agriculture
  • Climate change
  • Hazards
  • Oceanography
  • Inland waters
  • Drought severity
  • Wildfire risk
  • Spatial
  • Temporal
  • Libraries
  • Time-series

Published Papers (1 paper)

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Vegetation Growth Analysis of UNESCO World Heritage Hyrcanian Forests Using Multi-Sensor Optical Remote Sensing Data
Remote Sens. 2021, 13(19), 3965; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193965 - 03 Oct 2021
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Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, [...] Read more.
Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Phenological Libraries)
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